Sonar data are found useful for low-level interactions such as real-time obstacle avoidance, but typically are considered unfeasible for providing sensor modality for intelligent robotic interactions with the world. Nevertheless, bats display on a daily basis that sonar sensing does allow rich interactions with the environment by performing a wide array of interesting and intelligent behaviors.
They continuously prove that a sonar system can extract all the necessary information for intelligent interactions with the environment.
In order for a sonar system to be useful for navigation in real-life office environments, it should meet several criteria. First, the sensor should have a wide field of view (FOV), which is useful in navigational tasks in enclosed spaces. Due to the fact that acoustic waves in the ultrasonic range reflect specularly from large surfaces, only sound waves impinging on the normal direction to the surface are reflected back to the sonar sensor. For navigating a corridor, hallway or doorway having the wide FOV is crucial.
Next, the sensor should obtain spatial information on its environment using a single measurement. As the speed of sound in air is fairly slow (vs=343 m/s), a hard upper limit of the maximum measurement rate is constraining the system design. If one scans the environment using mechanical scanning and multiple measurements, the sonar sensor will inevitably have a low information update rate, which is why mechanical scanning should be avoided. Following naturally from this constraint, the amount of information that should be extracted from every measurement should be maximized. The operational complexity associated with mechanical systems further advocate the use of static sonar systems.
Finally, the system should be able to cope with overlapping echoes (i.e. echoes arriving simultaneously at the sensor). Overlapping echoes regularly occur during realistic indoor navigation tasks, for example while navigating through a doorway or a hallway (similar to the first constraint). Echoes from both edges of the doorway will, in an ideal situation (as the robot is driving straight through the door), arrives simultaneously at the sensor. The sensor should be able to distinguish multiple overlapping echoes without making false estimations of the positions thereof (for example, averaging the two echoes into one reflector in the middle).
Existing sonar technology can be roughly divided into two categories: biomimetic sonar systems which try to mimic bate cholocation and which heavily rely on spectrospatial cues introduced by the emitter and receivers, and classic sonar technology which use an array of sensors and differences in arrival times at each sensor to estimate the location of the reflectors.
Biomimetic sonar systems have been proposed several times before. All of these works use broadband emissions and some form of spatial filter, dubbed Head Related Transfer Function (HRTF). As the spectrum of the emission is approximately known in an active sonar system, the difference between the emitted and the received spectra can be calculated. The calculated differences can then be used to estimate the reflector position. The performances and limitation of this type of system has been studied in an information-theoretic context. One of the major limitations that these type of systems have is dealing with overlapping echoes. Simultaneous echoes severely interfere with each other, resulting in one distorted spectrum instead of multiple separate spectra. The distorted spectrum sometimes encodes the direction of the strongest reflector in the case of two reflectors with different strengths, but in the case of equal strong reflections (such as when driving in the middle of a doorway) the resulting spectrum encodes none of the real positions. Filtering introduced by the reflector also degrade the localization performance as most of the biomimetic models assume point-like reflectors with flat frequency responses.
The non-biomimetic sonar systems are not limited to the spectrum based localization algorithms, and can be constructed with any number of sensors and emitters. If only one narrow band sensor is used, only range information can be extracted from the measurement. The most famous example of this system is the Polaroid ultrasonic ranging system. It uses the Time Of Flight (TOF) of the sound waves to estimate the range to the nearest reflector. Building on these systems, clever ways of extracting bearing information have been devised.
For example, a configuration of 3 Polaroid sensors was suggested to provide an unbiased estimate of target positioning 2D based on TOF differences between the sensors. This TOF paradigm has been investigated with relative successes, making use of techniques such as correlation, matched filtering, PCA, etc. Another way of generating spatial information is to mechanically scan the environment using a ranging sensor and a pan/tilt unit. While the generated spatial maps contain high-resolution information, the mechanical scanning makes these type of sensors unfeasible in a robotic navigation application due to the limited information update rate.
Although mechanical scanning is unfeasible in a robotics application, electronic scanning using a phased array of receivers can be an interesting way of solving several issues. One generates a spatial filter using an array of closely spaced transducers and appropriate signal processing techniques, ranging from simple delay and sum beam forming to high resolution subspace beam forming techniques. This spatial filter can be steered into several directions in post processing, without the need for multiple measurements. Sonar systems using array technology have been proposed repeatedly in the literature. Although different types of arrays are proposed, using a variety of signal processing techniques and array topologies and both narrow band and broadband echolocation signals, there is still a need for a good echolocation system.